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2022 World Hypertension League, resolve to save lives and International Society of Hypertension dietary sodium (salt) global call to action

A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks

  • Authors: Khan, Zahoor , Amjad, Sana , Ahmed, Farwa , Almasoud, Abdullah , Imran, Muhammad , Javaid, Nadeem
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 11, no. (2023), p. 31036-31051
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  • Description: Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE.

A brief guide to multi-objective reinforcement learning and planning JAAMAS track

A community-wide approach to reducing risky drinking cultures in young people in rural Australia

  • Authors: Murphy, Angela , Ollerenshaw, Alison
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Australian Journal of Rural Health Vol. 31, no. 2 (2023), p. 204-217
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  • Description: Objective: This study evaluated the impact of a multi-faceted, harm minimisation program addressing youth alcohol change and risky drinking behaviours in rural Australia. The role and influence of a multi-tiered community approach to changing alcohol cultures is examined. Setting: An alcohol culture change project for young people (12–18 years) was implemented in rural Victoria. It was informed by the Alcohol Cultures Framework, comprising community-wide events and youth-focused activities, co-designed with young people. The approach aimed at maximising engagement and reducing alcohol-related harm by targeting the shared activities and drinking practices of young people, parents and the community. Participants: Participants (n = 446) provided feedback specific to three key program activities for promoting alcohol change. Design: Mixed methods: Feedback sheets were collected, and interviews and focus groups were conducted with program participants. Results: Participants indicated that the program had informed their understanding of the way people in their region drink, and the social norms and practices around alcohol that encourage risky drinking. It influenced their short- and medium-term reactions, learnings and activities relating to alcohol consumption. The impact of the program was greatest in adults than young people although reflective learning and some behaviour change were evident across all age groups and community clusters. Conclusion: Community-wide health promotion events offer participants a deeper understanding of the ways in which dominant alcohol cultures inform the practices and activities of young people within a broader community context. Ensuring health promotion programs within a whole-of-community approach are established longer term, is recommended. © 2022 The Authors. Australian Journal of Rural Health published by John Wiley & Sons Australia, Ltd on behalf of National Rural Health Alliance Ltd.

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

  • Authors: Bignold, Adam , Cruz, Francisco , Taylor, Matthew , Brys, Tim , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Ambient Intelligence and Humanized Computing Vol. 14, no. 4 (2023), p. 3621-3644
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  • Description: A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent’s performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

A depth-based hybrid approach for safe flight corridor generation in memoryless planning

  • Authors: Nguyen, Thai , Murshed, Mamzur , Choudhury, Tanveer , Keogh, Kathleen , Appuhamillage, Gayan , Nguyen, Linh
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 16 (2023), p.
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  • Description: This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning. © 2023 by the authors.

A framework for adoption decision process for blockchain technology - an institutional and actor-network theory perspective

  • Authors: Kaushik, Shipra
  • Date: 2023
  • Type: Text , Thesis , PhD
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  • Description: Blockchain has been the most promising technology of the recent times. Originated from bitcoin, a blockchain technology use case has now been explored across almost every industry. It provides several novel technological features like transparency, disintermediation, immutability, trust among stakeholders and decentralisation. Despite so many advantages, the overview of challenges around blockchain adoption has revealed that there is a scarcity of understanding about the process of blockchain adoption decisions. Several organisations have failed to take advantage of blockchain's potential due to uneven adoption across industries and regions. Whether to use blockchain in their business is a difficult decision for many organisations. To fill this gap, this study examined the adoption decision process of blockchain in organisations. Firstly, there is a need of framework that details the steps in the blockchain adoption decision process, including tasks involved and the rationales for the actions taken. This understanding will help the potential adopters to make a successful decision to adopt blockchain technology for their organisation. Secondly, very few studies have examined the factors that influence the stakeholders’ interactions and dynamics while making technology adoption decisions, especially in blockchain based applications. When systems are designed to protect privacy or obscure actors intentionally, such as blockchain platforms, it can be challenging to identify them and understand their roles. Blockchain being an inter-organisational technology, primarily depends on the involvement of internal and external stakeholders. Thus, this study explored the actors involved in the adoption decision process and their roles while aligning other actors towards blockchain adoption. Thirdly, as these actors act as stakeholders while making decision, they act as rational individuals. Therefore, this study also explored their rationales while they are involved in technology adoption decisions to have an effective outcome of the decision-making process. To achieve these objectives, this study utilises Innovation Translation approach derived from Actor-Network Theory and Institutional Theory for technology adoption. The study has utilised a three- round qualitative Delphi method through semi structured interviews to gather views from a panel of experts from organisations who have experienced the blockchain adoption decision process for their business. The targeted experts for this study were categorized as Adopters, Non-Adopters (dropped the idea) and Consultants using selective purposive sampling. The first two rounds were exploratory in nature, and to extend the validity of the responses gathered, the final round was a confirmatory round of interviews. For this study, the saturation was seen with ten experts in the panel for round 2 and round 3. For the pilot study eight participants agreed to be part of the panel. The interviews were recorded, transcribed and analysed using thematic analysis in the NVivo tool. The analysis confirmed the use of Innovation Translation approach in literature for understanding the actors and their roles, by giving a rich interpretation of the results in understanding the crucial interactions among the actors and drawing useful findings. The interpretation also provided an insight into the institutionalisation of blockchain by exploring the institutional pressures. The study has confirmed the existence of many pressures that existed for other technologies, remain for blockchain adoption too like hype, curiosity, competitiveness, business value, cost and time but has explored new institutional pressures with blockchain adoption decision process like understanding among consultants and adopting organisations, and process participations needs. Utilising Institutional Theory for blockchain technology has also revealed a fourth pressure that is exerted by the technology itself like maturity, consensus, network dominancy and technological features that are primarily seen as blockchain being an inter-organisational and a new technology that has not been accepted widely in organisations. Achieving the objectives of this study, the study has proposed a consolidated framework for the blockchain adoption decision process from an exploratory view. The first of its kind in literature, that elaborates on the stages involved in blockchain adoption decision process, identify the actors and explains their role at each stage and how those roles evolve and also provides an insight into the institutionalisation of blockchain by exploring the pressures. These gaps, objectives, method, analysis, and contributions are further discussed in this thesis comprehensively.
  • Description: Doctor of Philosophy

A fully automated self-help biopsychosocial transdiagnostic digital intervention to reduce anxiety and/or depression and improve emotional regulation and well-being: pre-follow-up single-arm feasibility trial

  • Authors: Klein, Britt , Nguyen, Huy , McLaren, Suzanne , Andrews, Brooke , Shandley, Kerrie
  • Date: 2023
  • Type: Text , Journal article
  • Relation: JMIR Formative Research Vol. 7, no. (2023), p.
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  • Description: Background: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. Objective: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. Methods: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). Results: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR] < .001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=

A hybrid PWM strategy for SMES integrated grid-feeding transformerless PV Inverters

A hydrogen supply-chain model powering Australian isolated communities

  • Authors: Hasan, Tanvir , Hassan, Nur , Shah, Rakibuzzaman , Emami, Kianoush , Anderson, Jake
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Energy Reports Vol. 9, no. (2023), p. 209-214
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  • Description: This article proposes a supply chain-based green hydrogen microgrid modelling for a number of remote Australian communities. Green hydrogen can be used as an emissions-free fuel source for electricity generation in places where large-scale renewable energy production is impossible due to land availability, population, or government regulations. This research focuses on the Torres Strait Island communities in northern Australia, where the transition from diesel to renewable electricity generation is difficult due to very limited land availability on most islands. Due to geographical constraints, low population and smaller electrical load, the green hydrogen needs to be sourced from somewhere else. This research presents a green hydrogen supply chain model that leverages the land availability of one island to produce hydrogen to supply other island communities. In addition, this research presents a model of producing and transporting green hydrogen while supplying cheaper electricity to the communities at focus. The study has used a transitional scenario planning approach and the HOMER simulation platform to find the least-cost solution. Based on the results, a levelised cost of energy range of AU$0.42 and AU$0.44 was found. With the help of a green hydrogen supply chain, CO2 emissions at the selected sites could be cut by 90 %. This study can be used as a guide for small clustered communities that could not support or justify large-scale renewable generation facilities but need more opportunities to install renewable generation. © 2023

A literature review of the positive displacement compressor : current challenges and future opportunities

  • Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Energies Vol. 16, no. 20 (2023), p.
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  • Description: Positive displacement compressors are essential in many engineering systems, from domestic to industrial applications. Many studies have been devoted to providing more insights into the workings and proposing solutions for performance improvements of these machines. This study aims to present a systematic review of published research on positive displacement compressors of various geometrical structures. This paper discusses the literature on compressor topics, including leakage, heat transfer, friction and lubrication, valve dynamics, port characteristics, and capacity control strategies. Moreover, the current status of the application of machine learning methods in positive displacement compressors is also discussed. The challenges and opportunities for future work are presented at the end of the paper. © 2023 by the authors.

A longitudinal study on a place-based school-university partnership : listening to the voices of in-service teachers

  • Authors: Ma, Hongming , Green, Monica
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Teaching and Teacher Education Vol. 129, no. (2023), p.
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  • Description: This paper reports on a longitudinal place-based study by two Australian teacher educators investigating their three-year science-based school-university partnership. The study examined key benefits, challenges, and tensions within the partnership. Data collection was drawn from focus group interviews with in-service teachers across each partnership year. While findings portray the partnership as a catalyst for increased science learning opportunities for school students, teaching opportunities for pre-service teachers, and new in-service teacher roles and responsibilities, the study highlights the evolving nature of partnership development, including the need for continuous negotiation of labor division and stakeholder expectations. © 2023 The Authors

A nethack learning environment language wrapper for autonomous agents

  • Authors: Goodger, Nikolaj , Vamplew, Peter , Foale, Cameron , Dazeley, Richard
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Open Research Software Vol. 11, no. (2023), p.
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  • Description: This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment. © 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

A new era of integration between multiomics and spatio-temporal analysis for the translation of EMT towards clinical applications in cancer

  • Authors: Fonseca Teixeira, Adilson , Wu, Siqi , Luwor, Rodney , Zhu, Hong-Jian
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Cells Vol. 12, no. 23 (2023), p.
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  • Description: Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients. © 2023 by the authors.

A novel dynamic software-defined networking approach to neutralize traffic burst

  • Authors: Sharma, Aakanksha , Balasubramanian, Venki , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Computers Vol. 12, no. 7 (2023), p.
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  • Description: Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. © 2023 by the authors.

A pilot comparison of fixatives for hookworm real-time polymerase chain reaction

  • Authors: Bradbury, Richard , Inagaki, Kengo , Singh, Gurbaksh , Agana, Urita , Patterson, Kayla , Malloch, Lacy , Rodriguez, Eduardo , Qvarnstrom, Yvonne , Hobbs, Charlotte
  • Date: 2023
  • Type: Text , Journal article
  • Relation: American Journal of Tropical Medicine and Hygiene Vol. 108, no. 2 (2023), p. 335-339
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  • Description: Polymerase chain reaction (PCR) is increasingly used in the diagnosis of soil-transmitted helminth infections. Despite this, few studies have evaluated the impact of different fecal fixatives on the outcome of fecal helminth qPCR analysis, and none have evaluated the effect of commercial parasitology fixatives commonly used in diagnostic laboratories. We fixed dog feces containing Ancylostoma spp. hookworm eggs in zinc polyvinyl alcohol (Zn-PVA) and Total-Fix, and with 70% ethanol (EtOH) as a control. DNA was extracted at timepoints 11, 33, 64, and 94 days and subjected to Ancylostoma spp. quantitative PCR (qPCR). A linear regression model was created to assess the effect of preservative types on the temporal change of qPCR quantification cycle number (Cq) values, accounting for variances among individual animals. Fixation in 70% EtOH least affected Cq values over 94 days. Total-Fix preservation yielded a higher Cq overall, but there was no significant difference compared with 70% EtOH fixation. Fixation in Zn-PVA resulted in significantly (P < 0.001) higher Cq values than 70% EtOH after only 33 days and loss of amplification at 64 days. Consistent with other helminth fixation studies, 70% EtOH performed well in preserving hookworm DNA over 94 days. Total-Fix provided a comparable alternative for qPCR analysis for hookworm. Fixation in Zn-PVA resulted in loss of detectable hookworm DNA at 64 days, as determined by qPCR. Copyright © 2023 The American Society of Tropical Medicine and Hygiene.

A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems

  • Authors: Pham, Tan , Dao, Minh , Shah, Rakibuzzaman , Sultanova, Nargiz , Li, Guoyin , Islam, Syed
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Numerical Algorithms Vol. 94, no. 4 (2023), p. 1763-1795
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  • Description: In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex function and a differentiable function with Lipschitz continuous gradient, subtracted by a weakly convex function. This general framework allows us to tackle problems involving nonconvex loss functions and problems with specific nonconvex constraints, and it has many applications such as signal recovery, compressed sensing, and optimal power flow distribution. We develop a proximal subgradient algorithm with extrapolation for solving these problems with guaranteed subsequential convergence to a stationary point. The convergence of the whole sequence generated by our algorithm is also established under the widely used Kurdyka–Łojasiewicz property. To illustrate the promising numerical performance of the proposed algorithm, we conduct numerical experiments on two important nonconvex models. These include a compressed sensing problem with a nonconvex regularization and an optimal power flow problem with distributed energy resources. © 2023, The Author(s).

A qualitative study on undergraduate student nurses’ experience of mental health simulation preclinical placement

  • Authors: Olasoji, Michael , Garvey, Loretta , Sadoughi, Navideh , Willetts, Georgina
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Clinical Simulation in Nursing Vol. 84, no. (2023), p.
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  • Description: Background: Simulations allow students to be challenged and supported while gaining both technical and non-technical skills within a clinical learning environment. Working in a mental health setting can be quite challenging and confronting at times for undergraduate nursing students in clinical placement. The study aims to explore nursing students’ perceptions of a mental health simulation workshop's impact before clinical placement, which provides a supportive environment to gain technical and non-technical skills while being challenged and supported. Sample: Participants were a second-year cohort (n = 89) of undergraduate nursing students enrolled in a mental health unit. Methods: Descriptive survey design. The researchers thematically analysed narrative responses of a pre- and post-simulation survey from an immersive simulation using a descriptive survey design. Results: The researchers identified six key themes: two from the pre-simulation survey – communication with and assessment of mental health patients, and the opportunity for placement preparation; and four from the post-simulation survey – the opportunity for debriefing, the realism of the simulation, increased confidence levels, and the perception of a safe learning environment. Conclusion: Effective skill acquisition is essential to advance recruitment and retention into mental health environments. The use of mental health simulation that comprises of realism and immersion working with simulated patients provided opportunity to advance this. © 2023

A reevaluation of the factor structure, reliability, and validity of the spiritual well-being questionnaire (SWBQ)

  • Authors: Gomez, Rapson , Watson, Shaun
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Religion and Health Vol. 62, no. 3 (2023), p. 2112-2130
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  • Description: The 20-item Gomez and Fisher (Personal Individ Differ 35:1975–1991, 2003) Spiritual Well-Being Questionnaire (SWBQ) is a widely used measure of spiritual well-being. Its theoretical model is a higher-order model with primary factors for personal, communal, environmental, and transcendental well-being, and a secondary global spiritual well-being factor. The current study, conducted in Australia, reevaluated the factor structure of the SWBQ. Unlike previous studies, the current study also used exploratory structural equation modeling (ESEM) to examine the factor structure of the SWBQ and selected the preferred model using not only global model fit values, but also the clarity, reliabilities, and validities of the factors in the models. A total of 227 adults (males = 63; females = 164; M age = 26.1 years; SD = 5.2 years) completed the SWBQ. Based on the model selection criteria applied in the study, the ESEM model with four group factors was selected as the preferred model. However, there was also adequate support for the proposed theoretical higher-order model and the first-order oblique model with the four well-being factors. Concerning our preferred model, its factors showed reasonable clarity for factor loadings and (omega) reliabilities. However, only the communal domain scale was supported empirically for external validity. The implications of the findings for the theoretical model, the use of the SWBQ, and future studies are discussed. In this respect, there are three potential models (theorized higher-order model, 4-factor first-order oblique model, and the ESEM model proposed in this study) that warrant further detailed investigation with a larger, more representative population and additional validation measures. © 2022, The Author(s).

A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing

  • Authors: Nasser, Nidal , Emad-ul-Haq, Qazi , Imran, Muhammad , Ali, Asmaa , Razzak, Imran , Al-Helali, Abdulaziz
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 19 (2023), p. 13775-13789
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  • Description: Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.